ordinalCont (version 0.4)

ocmm: Ordinal regression for continuous scales with mixed effects

Description

Fits an ordinal continuous mixed model with logit link, using the generalized logistic function as g function.

Usage

ocmm(formula, data = NULL, weights, start = NULL, link = c("logit"), gfun = c("glf"), method = c("optim", "ucminf"), quad = c("Laplace", "GH"), n_nodes = 10)

Arguments

formula

a formula expression as for regression models, of the form response ~ predictors. Only mixed-effects models with a single random effect on the intercept are supported. The model must have an intercept: attempts to remove one will lead to a warning and will be ignored.

data

an optional data frame in which to interpret the variables occurring in the formulas

weights

optional case weights in fitting. Defaults to 1.

start

a vector of initial values for the regression coefficients, M, B, T, (offset, slope and symmetry of the g function) and the standard deviation of the random effect

link

link function, i.e., the type of location-scale distribution assumed for the latent distribution. The default logit link gives the proportional odds model and is the only link function currently supported.

gfun

A smooth monotonic function capable of capturing the non-linear nature of the ordinal measure. It defaults to the generalized logistic function (g_glf), which is currently the only possibility.

method

The optimizer used to maximize the likelihood function.

quad

A string indicating the type of quadrature used to integrate over the random effects. Can take values "Laplace" (Adaptive Gauss-Hermite quadrature using Laplace approximation; the default) or "GH" (Gauss-Hermite quadrature).

n_nodes

order of Gauss-Hermite rule used (number of nodes)

Value

An object of type ocmm with the components listed below.

coefficients

parameter estimates. The first len_beta elements are the estimates of the fixed-effects parameters; the last 4 elements are the estimates of the parameters of the g function (M, B, and T) and the standard deviation of the random effect.

vcov

variance-covariance matrix, of dimension (len_beta +4)x(len_beta +4 )

sigma_rnd

standard deviation of the random effect

df

estimated degrees of freedom

logLik

value of the log-likelihood at the estimated optimum

len_beta

number of fixed-effects parameters of the model

len_gfun

number of parameters in the g function used in the model

len_rnd

number of random effects (1 in this version of the package)

fitted.values

fitted probabilities

residuals

residuals on the latent scale

v

vector of continuous scores

x

model matrix

sample.size

sample size (can differ from the number of observations if the weights are different from 1)

nobs

number of observations

call

call to fit the model

no.pars

total number of parameters estimated

data

data frame used

link

link function used

gfun

g function used

formula

formula used

Details

Fits a continuous ordinal regression model, with fixed and random effects. The g function is the generalized logistic function (see g_glf), and the link function is the logit, implying the standard logistic distribution for the latent variable. Maximum likelihood estimation is performed, using optim {stats} with a quasi-Newton method ("BFGS"). Either adaptive Gauss-Hermite quadrature with the Laplace approximation, or Gauss-Hermite quadrature, is used. For continuous ordinal modelling with fixed effects only, see ocm.

Examples

## Not run: 
# fit.overall.rnd  <- ocmm(overall  ~ cycleno + age + bsa + treatment + (1|randno), data=ANZ0001)
# fit.phys.rnd     <- ocmm(phys 	   ~ cycleno + age + bsa + treatment + (1|randno), data=ANZ0001)
# fit.pain.rnd 	  <- ocmm(pain 	   ~ cycleno + age + bsa + treatment + (1|randno), data=ANZ0001)
# fit.mood.rnd 	  <- ocmm(mood 	   ~ cycleno + age + bsa + treatment + (1|randno), data=ANZ0001)
# fit.nausvom.rnd <- ocmm(nausvom ~ cycleno + age + bsa + treatment + (1|randno), data=ANZ0001)
# fit.appetite.rnd <- ocmm(appetite ~ cycleno + age + bsa + treatment + (1|randno), data=ANZ0001)
# summary(fit.overall.rnd)
# summary(fit.phys.rnd)
# summary(fit.pain.rnd)
# summary(fit.mood.rnd)
# summary(fit.nausvom.rnd)
# summary(fit.appetite.rnd)
# ## End(Not run)